Both are taught at techcadd. Here is how they differ on level, tools, projects and where each one can take you.
| Feature | Machine Learning | Deep Learning |
|---|---|---|
| Level | Intermediate | Advanced |
| Duration | 4–6 Months | 3–4 Months |
| Who can join | Basic computer skills and school-level maths; no prior programming experience required | Basic computer skills and school-level maths; Python or machine learning knowledge is useful but not compulsory |
| Key tools | Python, Jupyter Notebook, Google Colab, NumPy, Pandas, SQL | Python, Jupyter Notebook, Google Colab, NumPy, Pandas, Matplotlib |
| What you’ll learn | Module 1: Python for Machine Learning · Module 2: Maths and Statistics Essentials · Module 3: Data Handling and Visualisation · Module 4: Supervised Learning | Module 1: Python and Maths for Deep Learning · Module 2: Machine Learning Foundations · Module 3: Neural Network Fundamentals · Module 4: Building and Tuning Models with TensorFlow, Keras and PyTorch |
| Portfolio projects | — | — |
| Career roles | Machine Learning Engineer (junior), Data Analyst, Junior Data Scientist | Deep Learning Engineer, Machine Learning Engineer, Computer Vision Engineer |
Start with Python, statistics and data handling, then move on to supervised and unsupervised learning, model evaluation and deploying simple models into real applications.
Learn how artificial neural networks, CNNs, RNNs, LSTMs and transformers learn from data, working in Python with TensorFlow, Keras and PyTorch on real datasets.
A counsellor can map either course to your degree, schedule and career goal — one call is usually enough.
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